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      Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data

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          Abstract

          The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.

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          Most cited references 69

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          Searching for a baseline: functional imaging and the resting human brain.

          Functional brain imaging in humans has revealed task-specific increases in brain activity that are associated with various mental activities. In the same studies, mysterious, task-independent decreases have also frequently been encountered, especially when the tasks of interest have been compared with a passive state, such as simple fixation or eyes closed. These decreases have raised the possibility that there might be a baseline or resting state of brain function involving a specific set of mental operations. We explore this possibility, including the manner in which we might define a baseline and the implications of such a baseline for our understanding of brain function.
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            Dynamic causal modelling.

            In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
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              Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI.

              In children with attention deficit hyperactivity disorder (ADHD), functional neuroimaging studies have revealed abnormalities in various brain regions, including prefrontal-striatal circuit, cerebellum, and brainstem. In the current study, we used a new marker of functional magnetic resonance imaging (fMRI), amplitude of low-frequency (0.01-0.08Hz) fluctuation (ALFF) to investigate the baseline brain function of this disorder. Thirteen boys with ADHD (13.0+/-1.4 years) were examined by resting-state fMRI and compared with age-matched controls. As a result, we found that patients with ADHD had decreased ALFF in the right inferior frontal cortex, [corrected] and bilateral cerebellum and the vermis as well as increased ALFF in the right anterior cingulated cortex, left sensorimotor cortex, and bilateral brainstem. This resting-state fMRI study suggests that the changed spontaneous neuronal activity of these regions may be implicated in the underlying pathophysiology in children with ADHD.
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                Author and article information

                Journal
                Front Syst Neurosci
                Front. Syst. Neurosci.
                Frontiers in Systems Neuroscience
                Frontiers Research Foundation
                1662-5137
                28 February 2010
                06 April 2010
                2010
                : 4
                Affiliations
                1Department of Clinical Neuroscience, Imperial College London London, UK
                2Department of Clinical Neurology, Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford Oxford, UK
                Author notes

                Edited by: Lucina Q. Uddin, Stanford University, USA

                Reviewed by: Thomas Meindl, University of Munich, Germany; Catie Chang, Stanford University, USA

                *Correspondence: Christian F. Beckmann, Department of Clinical Neuroscience, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK. e-mail: c.beckmann@ 123456imperial.ac.uk
                Article
                10.3389/fnsys.2010.00008
                2854531
                20407579
                Copyright © 2010 Cole, Smith and Beckmann.

                This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

                Page count
                Figures: 5, Tables: 0, Equations: 0, References: 124, Pages: 15, Words: 14671
                Categories
                Neuroscience
                Review Article

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